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Item Investigation of Dust Emission in Limestone Mines and its Statistical Prediction using Supervised Machine Learning (Regression) Modelling(World Researchers Associations, 2025) Rajib, P.; Harsha, V.; Shanmugam, S.B.; Harish, H.; Amrites, S.In India, the fugitive dust emissions in the processing plant and mining area of limestone mines are very high. The dust emission of (particulate matter) PM10 and PM2.5 forms an unsafe working environment for workers in processing plant areas and mining areas. The excessive emission of PM10 and PM2.5 will cause lung-related diseases to the workers and the people existing in the adjacent areas of the mine. The dust emission majorly causes air pollution to occur due to the distribution of particulate matter in the work area. This study majorly investigates the dust emission levels of PM10 and PM2.5 in the limestone mine of Kadapa, Andra Prasad, India. The investigation on the dust emission of PM10 and PM2.5 was carried out as per the guidelines of DGMS and MoEF and CC guidelines, with a specific focus on PM10 and PM2.5 particulate matter. From the study, it was clear that the dust emission levels of PM10 and PM2.5 in the mine area and some parts of the processing area were below the permissible limit of 1200 ?g/m³ as per the National Ambient Air Quality Standards (NAAQS, 2009). It was also found that the dust emission levels of PM10 and PM2.5 in the crushing and screening area of the processing plant were above the permissible limit of 1200 ?g/m³. Further the statistical prediction model was developed using linear, quadratic and cubic supervised machine learning (regression) modelling. The results indicated that the cubic regression model will provide the accurate prediction of fugitive dust emission with lower error and standard deviation. © 2025, World Researchers Associations. All rights reserved.Item Development of beneficiation circuit for low-grade laterite iron ores sourced from the Gujarat area(World Researchers Associations, 2025) Reddy, B.R.R.; Harsha, V.; Bhushan, A.S.; Harish, H.; Shanmugam, S.B.This study focuses on the maximum recovery of iron values from the low-grade laterite iron ore. The Fe analysis of laterite was carried out using wet method analysis. Subsequently, the characterization studies were carried out on laterite ore using Optical microscope for liberation studies, mineral phase analysis with XRD and elemental analysis using SEM-EDS. Further, the ore of feed particle size of-150 microns was subjected to physical separation techniques such as scrubbing, hydro cyclone, spiral concentrator and dual-stage HGMS and two beneficiation circuits. The results from the above physical separation beneficiation techniques showed a concentrate of 41.25% FeG and a recovery of 48.05% in beneficiation circuit 1 and a concentrate of 48.03 % FeG and a recovery of 62.11% in beneficiation circuit 2 which is not feasible for iron-making in the blast furnace. © 2025, World Researchers Associations. All rights reserved.